class_docstr: Normalize a tensor image with mean and standard deviation
  
  Given mean: m and std: s
  will  normalize each channel as channel = (channel - mean) / std
  
  Args:
      mean (int): mean value
      std (int): std value
code: def __init__(self, mean, std):
      self.mean = mean
      self.std = std
  
file_path: gluon-cv/gluoncv/torch/data/transforms/videotransforms/tensor_transforms.py
imports: import random
  from .utils import functional
label_desc: Use image node_id 1 to create a new Normalize object from inherited base classes: object with example: obj = Normalize(mean, std)